A real retail catalog, read the way it's actually built — and the one defect hiding across it.
This is a real example of what EKOM's resolution layer surfaces on a live retail catalog — anonymized, with the client's name and its own house brands removed. The company is a national outdoor retailer running four brand properties across two markets. EKOM read the client's catalog, auto-profiled with no schema and no manual setup, and surfaced 234 findings — each one then checked against the raw source file.
These are machine-surfaced findings — a triage signal, not a verified defect list, and a small share may be intentional. The point of the study isn't the count: it's that a catalog can pass every completeness check and still carry high-consequence defects invisible to standard validation — and that reading it structurally is what makes both the defect and the fix legible.
Read through 25 recursive passes with no schema — and validated against the raw source file.
The three that carry the weight: 1,132 women's items tagged male · 2,816 descriptions with stored encoding corruption · ~1,030 in-stock items that can't compute shipping. After the source check, the barcode item resolved to a clean-export fix and one shared-barcode pattern was set aside — the catalog & content findings carry the weight.
Defects in the data itself — present regardless of export format or encoding. Tagged by who feels it.
A house-brand 1,000-lumen flashlight description opens with the copy for its 300-lumen sibling — wrong specs on a live listing. 53 men's shirts (a third-party workwear brand) carry Style = "Front Pocket Wallets", leaked from another category; 72 listings misspell a product-line name; and color fields contradict titles on multiple items. The shopper reads information that belongs to a different product.
~1,030 in-stock items carry zero weight and zero dimensions, so checkout can't compute a shipping rate — buyable until the cart tries to price shipping, then it fails. Alongside them: a folded pair of jeans declared 130 in long, 35 items under 0.2 oz, 30 items marked out of stock while holding positive inventory, and warehouse staging codes prefixed ++ surfacing as customer-facing titles on ammunition items.
™ is stored as â„¢ across 2,816 descriptions, with stray  characters and double-encoded entities. The source file is valid UTF-8 and still shows the corruption — so it's in the data, reaching shoppers in product copy, browser tabs, and search snippets.
Structural, encoding, and export-level items — each read against the raw source file and given a verdict. Sorting a real defect from an export artifact is the difference between "fix your export" and "fix your catalog."
| Item | Verdict | What the source showed | Owner |
|---|---|---|---|
| Barcodes in scientific notation | Export artifact | Barcodes rendered as 8.06865E+11 — a spreadsheet display of a long number; the digits are intact in the system of record. | Clean re-export |
| Character-encoding corruption | Confirmed | Valid UTF-8 still shows â„¢ / Â across 2,816 descriptions — stored, not a reading mismatch. | EKOM applies |
| Formula errors in titles | In feed | Literal #NAME? on 29 items in HTML & Metadata Title; Excel-origin, titles lost in this export. | Re-export / rebuild |
| Empty structured columns | Confirmed gap | MPN, Ratings, isSale are 100% empty (Age Range 99.9%) — present but carrying no data. | Client decision |
| Shared barcode across variants | Set aside | Only two clean cases, one a duplicate row — did not hold up; the scientific-notation masking prevents assessment from this export. | — |
Confirmed defects EKOM resolves; feed/format items a clean source export settles fast.
| Action | What it covers | Owner |
|---|---|---|
| EKOM applies — confirmed data defects | ||
| Gender / category re-tag | The 1,132 women's/ladies'/girls' items tagged male, plus the smaller men's-tagged-female mirror. | EKOM applies |
| Content corrections | Copied flashlight description, 53 mistagged Style values, 72 misspellings, color/title contradictions, ++ staging codes. | EKOM applies |
| Encoding cleanup | Repair â„¢→™, stray Â, and double-encoded entities across ~2,816 descriptions. | EKOM applies |
| Fulfillment values | Flag the ~1,030 zero-dimension in-stock items and 30 false out-of-stock records for correction. | EKOM applies |
| Settled by a clean source export | ||
| Re-export barcodes as text | Resolves the scientific-notation barcodes (89% of the file) from the system of record — no catalog change. | Client / vendor |
| Rebuild the 29 #NAME? titles | A clean re-export may restore them; otherwise EKOM regenerates the affected titles. | Client + EKOM |
| Client decision | ||
| Missing identifiers | Confirm whether MPN and product ratings live in another system or should be sourced. | Client decision |
Why category-specific problems surface here rather than averaging away in a whole-catalog scan.
The gap that matters most isn't a blank field — it's a filled one that's wrong. Gender was populated on most of the catalog, and where it was populated it was frequently populated incorrectly. A completeness check passes it; only a read that weighs the value against the rest of the record catches it. That same read is what makes the next step precise — EKOM knows exactly which fields to enrich, and which are already sound.
This pass read and diagnosed. The same structural understanding powers the work that follows — turning a diagnosed catalog into one that captures the demand it's built for.
This case study is anonymized: the client's name and its own house-brand names have been removed; third-party manufacturer brands and the findings themselves are unchanged. EKOM read the client's catalog with no schema and no manual setup, through 25 recursive passes, and validated every finding against the raw source file. It was the second pass on this catalog — an earlier pass on a smaller slice predicted the gender default was systemic; this pass confirmed it.